Assessment of Personality from Interview Answers using Word Embedding Model in Machine Learning
The term "personality" refers to a unique way of thinking, acting and behaving. Personality includes moods, feelings and values which is often manifested visibly in interpersonal relations. It includes both natural and learned behavioural traits that set one individual different from other and are readily apparent when conversing with others in one's immediate surroundings and social circle. For the advancement of proper healthy dialogue, a plethora of strategies for determining nominee personalities have been built depending on the meaning of their textual message. From study it has been found that textual content of interview answers from standard interview question is a good metric for predicting someone’s personality trait. Nowadays, personality prediction has gotten a lot of attention. It analyzes user behavior and represents their thoughts, emotions, and so on. Traditionally, determining a personality attribute was a time-consuming method. Thus, for a large number of users, automated prediction is needed. Different methods use a variety of different machine learning algorithms, data sources, and feature sets. Personality prediction has developed into a major research field not just in psychology, but also in computer science, as a means of determining one's personality. This article conducts a detailed study of previous studies on personality prediction and discussed how a word embedding model may be used to classify candidate personality traits.